NuMA vs absense of NuMA in syncronized cells
Gianluca Alessio Mariani
26 June, 2025
Last updated: 2025-06-26
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Knit directory: mapelli_numa/
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knitr::opts_chunk$set(echo = FALSE,
message = FALSE,
warning = FALSE,
cache = FALSE,
autodep = TRUE,
fig.align = 'center',
fig.width = 10,
fig.height = 8)Synchronized NuMA- vs Synchronized NuMA+
The objective of this report is to investigate differential gene expression between the two conditions and to conduct gene ontology enrichment analysis to explore the biological functions involved.
Parameters
Below is the list of parameters used in this report to define differential gene expression.
- min_sample = 2 , minimum number of samples where the gene needs to have at least 1 read;
- logfc = 1
- qvalue = 0.05 , Adjusted p-value threshold (false discovery rate)
🧬 Comparison Group and Differential Gene Expression Analysis Plan
Below we show the comparison group considered for the analysis presented in this report. Each group contains all the samples associated to the specific condition we want to conduct the analysis on.
The group is divided into experimental samples and control samples.
Each differential gene expression comparison will be conducted between these two groups.
Group considered:
- Experimental samples
(Synchronized NuMA-,
e):
S61885_S-NuMA_A
S61886_S-NuMA_B
S61887_S-NuMA_C
-
Control Samples (Synchronized NuMA+,
c/tc):
S61882_S_plus_NuMA_A
S61883_S_plus_NuMA_B
S61884_S_plus_NuMA_C
The RNAseq data for this analysis:
- aim to investigate what is the difference in gene expression between Synchronized NuMA- and Synchronized NuMA+
The sample population include:
- 6 samples, 2 conditions, Synchronized NuMA- and Synchronized NuMA+ in 3 replicates each.
Background Correction
No technical control samples are present, so no background correction is applied. All analyses are based on normalized counts and a model that considers only the experimental condition. Lowly expressed genes are removed to reduce noise. Lowly expressed genes are here considered as:
- genes having total number of reads less than half of the samples, 3 ;
- genes expressed in less samples than the number of conditions, 2 .
PCA
Below we present the PCA analysis conducted on the two specific conditions analyzed in this report.
Interpretation PCA Analysis
Although the samples do not form perfectly distinct clusters, the main Principal Component clearly separates the experimental and control groups. This supports the conclusion that the samples are valid.
MA plot and volcano plot
MA plot
The MA plot is a widely used visualization in differential expression analysis that displays the relationship between the average expression (A) and the log fold change (M) for each gene. The x-axis represents the mean expression level across samples, while the y-axis shows the log fold change between groups.
Total number of significant genes: 266
Volcano plot
The Volcano plot is a graphical method to visualize differential expression results by combining statistical significance and magnitude of change for each gene. It plots the log2 fold change on the x-axis against the negative log10 of the p-value (or adjusted p-value) on the y-axis.
Tables of genes
Below we present two tables, the first includes all the genes identified in the analysis while the second includes only the differentially expressed genes (DEG)
Table of all genes
The columns in the table are:
baseMean: The average normalized count of a gene across all samples, reflecting its overall expression level in the dataset.
log2FoldChange: The estimated log2-transformed fold change in expression between two conditions (e.g., experimental vs control). Positive values indicate upregulation, negative values indicate downregulation.
lfcSE: The standard error associated with the log2 fold change estimate, indicating the variability or uncertainty of the fold change measurement.
stat: The test statistic value calculated for the hypothesis test of whether the log2 fold change differs from zero.
pvalue: The raw p-value corresponding to the statistical test for differential expression; it reflects the probability of observing the data assuming no true difference in expression.
padj: The p-value adjusted for multiple testing (e.g., using the Benjamini-Hochberg method) to control the false discovery rate (FDR), providing a more reliable significance measure.
comparison_exp_vs_contr: A label or descriptor indicating the comparison made, specifying which condition is experimental and which is control.
gene: The unique Ensembl identifier for each gene as annotated in the reference genome.
symbol: The gene symbol or common gene name, which is easier to interpret biologically than numerical IDs.
FoldChange: The fold change in linear scale (non-logarithmic), derived from log2FoldChange (i.e., 2^(log2FoldChange)), representing how many times expression has changed.
differentially_expressed: A categorical variable indicating whether the gene is considered differentially expressed (e.g., “yes” or “no”) based on the predefined thresholds for significance and fold change described in the next section.
Table of differentially expressed genes
In this table we can find a subset of the previous table that includes the differentially expressed genes (DEGs).
The genes defined as DEGs need to satisfy these two conditions:
- The associated padj (p-value adjusted for multiple testing) must be inferior to the qvalue of 0.05 ;
- The associated log2FoldChange absolute value must be superior to the logfc value of 1 .
Heatmaps
Given the significant genes, among the differentially expressed genes previously computed, let’s visualize the top20 and all the DE genes.
Meaning of Colors
- Red: Indicates high expression for that gene in a given sample (value above average, positive compared to the standardized scale).
- Blue: Indicates low expression for that gene in a given sample (value below average, negative compared to the standardized scale).
- White (or intermediate color): Indicates an expression close to the average (standardized value around 0).
Heatmap for the top 20 genes
This heatmap presents the expression profiles of the top 20 genes showing the most statistically significant differential expression, ranked by their adjusted p-values (padj). These genes represent the strongest candidates for biologically relevant changes between conditions. Displaying normalized and scaled expression values across samples, this focused visualization highlights the distinct expression patterns of the most significant genes, facilitating interpretation of key transcriptional differences driving the experimental effects.
Heatmap for all genes
This heatmap displays the expression levels of all genes detected in the RNA-seq dataset across all samples. The values are normalized and transformed (e.g., via variance stabilizing transformation) to allow comparison across genes and samples. This comprehensive visualization provides an overview of the global expression patterns, highlighting overall similarities and differences between samples, as well as potential outliers.
Gene set enrichment analysis
Further analysis is done through gene set enrichment analysis, which does not exclude genes based on logfc or adjusted p-value, as done previously. GSEA is performed separately on each subontology: biological processes (BP), cellular components (CC) and molecular functions (MF). The dot plot below shows all the enriched GO terms. The size of each dot correlates with the count of differentially expressed genes associated with each GO term. Furthermore, the color of each dot reflects the significance of the enrichment of the respective GO term, highlighting its relative importance.
GO - Biological Processes
Synchronized NuMA- vs Synchronized NuMA+
P value cutoff: 0.01
GO - Cellular Components
Synchronized NuMA- vs Synchronized NuMA+
P value cutoff: 0.01
GO - Molecular Functions
Synchronized NuMA- vs Synchronized NuMA+
P value cutoff: 0.01
Over representation analysis
We performed a functional enrichment analysis based on Over-Representation Analysis (ORA) using the Reactome pathway database. Unlike GSEA, which considers the entire ranked list of genes, ORA focuses only on genes that meet specific differential expression thresholds (e.g., adjusted p-value and log2 fold change). The analysis was conducted separately for upregulated and downregulated genes to identify Reactome pathways that are significantly enriched in each group, compared to what would be expected by chance. This allows for a clearer biological interpretation of distinct transcriptional programs activated or suppressed in the dataset. The dot plots below display all significantly enriched Reactome pathways. Each dot’s size represents the number of differentially expressed genes associated with the pathway, while the color reflects the statistical significance of the enrichment (adjusted p-value).
ReactomePA - UP
Synchronized NuMA- vs Synchronized
NuMA+
ReactomePA - DOWN
Synchronized NuMA- vs Synchronized
NuMA+
R version 4.5.0 (2025-04-11)
Platform: x86_64-pc-linux-gnu
Running under: Ubuntu 24.04.2 LTS
Matrix products: default
BLAS: /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3
LAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.26.so; LAPACK version 3.12.0
locale:
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[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C
time zone: Etc/UTC
tzcode source: system (glibc)
attached base packages:
[1] stats4 grid stats graphics grDevices utils datasets
[8] methods base
other attached packages:
[1] ReactomePA_1.53.0 CorLevelPlot_0.99.0
[3] tibble_3.3.0 limma_3.65.1
[5] org.Hs.eg.db_3.21.0 AnnotationDbi_1.71.0
[7] git2r_0.36.2 gridExtra_2.3
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[13] clusterProfiler_4.17.0 reshape_0.8.9
[15] DT_0.33 gplots_3.2.0
[17] RColorBrewer_1.1-3 rtracklayer_1.69.0
[19] DESeq2_1.49.1 SummarizedExperiment_1.39.0
[21] Biobase_2.69.0 MatrixGenerics_1.21.0
[23] matrixStats_1.5.0 GenomicRanges_1.61.0
[25] GenomeInfoDb_1.45.4 IRanges_2.43.0
[27] S4Vectors_0.47.0 BiocGenerics_0.55.0
[29] generics_0.1.4 ComplexHeatmap_2.25.0
[31] plotly_4.10.4 ggplot2_3.5.2
[33] yaml_2.3.10 workflowr_1.7.1
loaded via a namespace (and not attached):
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[4] bitops_1.0-9 ggplotify_0.1.2 R.oo_1.27.1
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[34] nnet_7.3-20 yulab.utils_0.2.0 rappdirs_0.3.3
[37] tweenr_2.0.3 circlize_0.4.16 enrichplot_1.29.1
[40] ggrepel_0.9.6 tidytree_0.4.6 reactome.db_1.92.0
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[55] jsonlite_2.0.0 GetoptLong_1.0.5 tidygraph_1.3.1
[58] Formula_1.2-5 survival_3.8-3 iterators_1.0.14
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